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Major streamlining achievement: - Eliminate 2,733 lines of duplicate content across commands - Reduce individual command files by ~70% (130-150 → 35-60 lines) - Leverage existing shared/*.yml reference patterns - Maintain full Claude Code compliance Benefits: • Single source of truth for universal content • Guaranteed consistency across all commands • Dramatically reduced maintenance overhead • Massive token efficiency improvements Implementation: - Universal Legend: @include shared/universal-constants.yml#Universal Legend - Universal Flags: @include shared/flag-inheritance.yml#Universal_Always - Command patterns: References to appropriate shared/*.yml files - Template system: Enhanced command-patterns.yml 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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/spawn - Spawn focused agent for specialized tasks
Legend
@include shared/universal-constants.yml#Universal_Legend
Purpose
Spawn specialized sub-agents to handle specific tasks in $ARGUMENTS with focused expertise and parallel execution capabilities.
Syntax
/spawn [flags] [task-description]
@include shared/flag-inheritance.yml#Universal_Always
Core Flags
--agent flag:
- researcher: Deep research & analysis
- builder: Code generation
- reviewer: Code review & QA
- optimizer: Performance tuning
- documenter: Documentation expert
--mode flag:
- sequential: One agent at a time
- parallel: Multiple agents
- collaborative: Agents work together
- supervisor: Oversee sub-agents
--scope flag:
- focused: Single specific task
- broad: Multiple related tasks
- exploratory: Open-ended research
- iterative: Refine through cycles
Agent Types
Researcher Agent:
- Deep dive into topics
- Compare solutions
- Analyze trade-offs
- Find best practices
- Document findings
Builder Agent:
- Generate code
- Implement features
- Create tests
- Build prototypes
- Integrate systems
Reviewer Agent:
- Code quality checks
- Security analysis
- Performance review
- Best practice validation
- Suggest improvements
Optimizer Agent:
- Performance profiling
- Resource optimization
- Algorithm improvements
- Database tuning
- Cache strategies
Documenter Agent:
- API documentation
- User guides
- Code comments
- Architecture docs
- README files
Execution Modes
Sequential Mode:
Flow: Agent1 → Agent2 → Agent3
Use: When tasks depend on each other
Example: Research → Build → Review
Parallel Mode:
Flow: Agent1 | Agent2 | Agent3
Use: For independent tasks
Example: Multiple feature builds
Collaborative Mode:
Flow: Agents work together
Use: Complex problems
Example: System design session
Best Practices
Task Definition:
- Clear objectives
- Specific deliverables
- Success criteria
- Resource limits
- Time constraints
Agent Selection:
- Match expertise to task
- Consider dependencies
- Plan coordination
- Set boundaries
- Define handoffs
Coordination:
- Clear communication
- Shared context
- Progress tracking
- Result integration
- Quality control
Examples
# Research then implement
/spawn --agent researcher "OAuth 2.0 best practices"
/spawn --agent builder "Implement OAuth based on research"
# Parallel feature development
/spawn --mode parallel --agent builder "User auth, Profile API, Settings UI"
# Full cycle with review
/spawn --mode sequential "Research → Build → Review payment integration"
# Collaborative system design
/spawn --mode collaborative --ultrathink "Design microservices architecture"
Integration
Works with:
- All command flags pass through
- Inherits persona preferences
- Shares project context
- Accumulates findings
- Coordinates outputs
Deliverables
- Agent execution logs
- Task completion reports
- Integrated results
- Performance metrics
- Lessons learned
- Handoff documentation